A Comparison of Extended Kalman Filter, Sigma-Point Kalman Filter, and Particle Filter in GPS/INS Sensor Fusion

Gross, Jason N.; Gu, Yu; Gururajan, Srikanth; Seanor, Brad; Napolitano, Marcello R.

doi:10.2514/6.2010-8332

Cited by 36 publications

(28 citation statements)

References 11 publications

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“…The UKF algorithm along with implementation guidelines is available in many publications within the literature, including textbooks 23,24 , as well as many articles in which a UKF is used specifically for an aerospace application 2,3,25 . In order to describe the sensor formulation utilized in this study 26 , the state vector, x, state transition equations, f(x k-1|k-1 ,u k), along with its input vector, u, are described, as well as the observation function, y=h(x k|k-1 ), and the measurement vector, z.…”

Section: B Gps/ins Sensor Fusion With On-line Imu Calibrationmentioning

confidence: 99%

On-line Modeling and Calibration of Low-Cost Navigation Sensors

Gross

Rhudy³

et al. 2011

AIAA Modeling and Simulation Technologies Conference

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In this paper, calibration modeling of a low-cost Inertial Measurement Unit (IMU) sensor for Small Unmanned Aerial Vehicle (SUAV) attitude estimation is considered. First, an Allan variance analysis method is used to determine stochastic noise model parameters for each sensor of a Micro-Electro-Mechanical-System (MEMS) IMU. Next, these models are included in a Global Positioning System/Inertial Navigation System (GPS/INS) sensor fusion algorithm for on-line calibration. In addition, an off-line magnetometer calibration is considered that uses a set of GPS/INS sensor fusion attitude estimates to derive a calibration model. This off-line magnetometer calibration model is then augmented on-line with sensor fusion estimates of the residual sensor biases. Finally, using the calibrated magnetometers, attitude estimation is considered that uses only a low-cost IMU with magnetometers. Each sensor fusion algorithm is formulated using an Unscented Kalman Filter (UKF). For performance validation, attitude estimates are calculated with data collected on-board a SUAV and are compared with high-quality vertical gyroscope measurements. NomenclatureA = Allan variance a x,y,z = specific-force in the aircraft body-axis (m/s 2 ) B = body-axis b = sensor bias f = state transition function h = observation function F s = sampling frequency g = acceleration due to gravity (m/s 2 ) j = counting index k = discrete-time index M = magnetometer measurement n = white driving noise p = roll rate (deg/s) q = pitch rate (deg/s) r = yaw rate (deg/s) x = position in the local South direction (m) x = state vector y = position in the local East direction (m) z = position in the local up direction (m) z = measurement vector Q = process-noise covariance matrix R = measurement-noise covariance matrix 2 T = averaging time (s) Ts = sampling time (s) u = input vector V x,y,z = velocity in the local south, east, up directions (m/s) w = wide-band noise Ω = averaged bin Θ = parameter vector θ = aircraft pitch angle (deg) σ = standard deviation τ = correlation time (s) ϕ = aircraft roll angle (deg) ψ = aircraft heading angle (deg)

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Section: B Gps/ins Sensor Fusion With On-line Imu Calibrationmentioning

confidence: 99%

On-line Modeling and Calibration of Low-Cost Navigation Sensors

Gross

Rhudy³

et al. 2011

AIAA Modeling and Simulation Technologies Conference

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“…This paper presents a continuation of previous GPS/INS sensor fusion research conducted at WVU [24][25][26][27][28]. Early work on this topic involved a baseline comparison of a single GPS/INS formulation using an EKF, an UKF, and a particle filter on a single set of flight data [24]. Using this same formulation, but incorporating more sets of flight data, a later study began to explore the sensitivity of this formulation for EKF and UKF in response to various phenomena [25].…”

Section: Introductionmentioning

confidence: 97%

“…The use of diverse flight data, along with the availability of independent "truth" measurements, provides a realistic and quantifiable evaluation of estimation algorithms. This paper presents a continuation of previous GPS/INS sensor fusion research conducted at WVU [24][25][26][27][28]. Early work on this topic involved a baseline comparison of a single GPS/INS formulation using an EKF, an UKF, and a particle filter on a single set of flight data [24].…”

Section: Introductionmentioning

confidence: 98%

Sensitivity Analysis of Extended and Unscented Kalman Filters for Attitude Estimation

Rhudy

Gross

et al. 2013

Journal of Aerospace Information Systems

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The extended Kalman filter (EKF) and unscented Kalman filter (UKF) for nonlinear state estimation with both additive and nonadditive noise structures are presented and compared. Three different Global Positioning System (GPS)/inertial navigation system (INS) sensor fusion formulations for attitude estimation are used as case studies for the nonlinear state estimation problem. A diverse set of actual flight data collected from research unmanned aerial vehicles was used as empirical data for this study. Roll and pitch estimation results were compared with independent measurements from a mechanical vertical gyroscope to evaluate the performance. The performance of the EKF and UKF is compared in terms of noise assumptions, covariance matrix tuning, sampling rate, initialization error, GPS outages, robustness to inertial measurement unit bias and scale factors, and linearization. Similar sensitivity for this GPS/INS attitude estimation problem was found between the EKF and UKF for most cases. Small differences were seen between EKF and UKF for initialization error and GPS outages: the UKF was found to be more robust to inertial measurement unit calibration errors, and the EKF was determined to be more computationally efficient.

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“…Safety of the research aircraft is reinforced by several hardware redundancies on critical components. Software in the follower aircraft's Gen-V system uses an Extended Kalman Filter (EKF) to provide attitude estimation and reduce position uncertainty [45].…”

Section: Phastball: Fixed Wing Test Bedmentioning

confidence: 99%

Autonomous Close Formation Flight Control with Fixed Wing and Quadrotor Test Beds

Rice

Huo

et al. 2016

International Journal of Aerospace Engineering

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Autonomous formation flight is a key approach for reducing energy cost and managing traffic in future high density airspace. The use of Unmanned Aerial Vehicles (UAVs) has allowed low-budget and low-risk validation of autonomous formation flight concepts. This paper discusses the implementation and flight testing of nonlinear dynamic inversion (NLDI) controllers for close formation flight (CFF) using two distinct UAV platforms: a set of fixed wing aircraft named "Phastball" and a set of quadrotors named "NEO." Experimental results show that autonomous CFF with approximately 5-wingspan separation is achievable with a pair of low-cost unmanned Phastball research aircraft. Simulations of the quadrotor flight also validate the design of the NLDI controller for the NEO quadrotors.

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A Comparison of Extended Kalman Filter, Sigma-Point Kalman Filter, and Particle Filter in GPS/INS Sensor Fusion

Cited by 36 publications

References 11 publications

On-line Modeling and Calibration of Low-Cost Navigation Sensors

On-line Modeling and Calibration of Low-Cost Navigation Sensors

Sensitivity Analysis of Extended and Unscented Kalman Filters for Attitude Estimation

Autonomous Close Formation Flight Control with Fixed Wing and Quadrotor Test Beds

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